Urban planners — the local variety of central planner — have a general suspicion of spontaneous order.
How could a process where a person risking his own money and reputation to develop a parcel of land to make a profit ever be superior to requiring that potential developer to produce a proposal to be studied for three years by well-trained professional planners at city hall, supported by a $500,000 contract to an outside consultant, written up in a 413-page environmental impact report, subject to a series of public hearings where people proffer their strongly-held opinions about what to do with someone else’s property, and then litigated in court for a decade?
The late classical liberal economist, Friedrich Hayek, argued that central planners are fatally conceited if they think that, by using centralized statistical data — data that is necessarily abstracted from reality — they can produce “a more efficient allocation of societal resources” than could a market economy. Urban planners don’t much care about price signals. But prices signal worth for land, commodities and labor more accurately than can bureaucrats.
How might spontaneous order look in the realm of urban development? An example: if a developer sees an area adding thousands of houses an hour away from a major employment area, perhaps, if given an opportunity, that developer might build a commercial center close to where people live. Thus, some workers could have the opportunity to work close to home in housing that might be more affordable than that closer to the central urban area.
Turning to your commute, what determines its length? It’s a simple calculation of how far you live from work and the average speed of travel to your destination. In some cities, you might crawl through seven miles of traffic in 40 minutes; while out in the countryside, you might cover 20 miles in less than 20 minutes.
Among the 50 states, the average commute time from 2006 to 2010 was about 23 minutes and 18 seconds. Generally, more urbanized places see longer commute times. New York, Maryland and New Jersey have the top three longest commute times in the nation, with commuters in the first two states taking an average of 31 minutes and 18 seconds to get to or from work. New York State is heavily urbanized, of course, with some 87.9 percent of its residents living in urban areas. At the other end of the spectrum are North Dakota, South Dakota and Montana. Some 59.9 percent of North Dakotans live in urban areas and they see an average commute time of just 16 minutes.
Plotting out commute times and the degree of urbanization shows some degree of correlation, though not as strong as many would think.
But, what if something other than the percentage of people living in an urban setting drove commute times? What if property rights mattered to the commute? Do states where landowners are freer to respond to market demand have shorter commutes? The next chart would suggest that land use restrictions might be more important than urbanization in predicting the commute times in a state.
At this point, boosters of government’s power over property rights might be tempted to claim that property rights are generally respected in rural states, but that restrictions are needed to cope with density; that planning necessarily takes precedence over respecting property. This next chart explores the relationship between urbanization and property rights, showing only a modest connection between weaker property rights and more urbanized states. For instance, heavily urbanized Nevada respects property rights far more than rural states such as Maine and Vermont.
To urbanization, property rights, and commute times, we might want to add a fourth factor: spending money on building roads, as opposed to mass transit or other items. This chart shows that the amount of money spent to lay down concrete is a stronger predictor of commute times than is urbanization, but not as strong a predictor as property rights.
Throwing all of these factors together in a multivariate regression test with commute times as the dependent variable shows that land use freedom and road spending are far more reliable predictors of the average commute time in a state than how urbanized that state is — statistically, it’s not even close.
| a. Dependent Variable: Time to commute in states, 2006-2010 (U.S. Census Bureau) | Adjusted R-Squared | ||||
| b. Predictors: (Constant) Mercatus Land use freedom index, 2011; percent of urbanization in state, 2010; per capita outlays on capital highway projects in states in 2010 and 2011 | 0.597 | ||||
| ANOVAa | Sum of Squares | Mean Square | F | Significance | |
| Regression Residual Total | 364.570 | 121.523 | 25.173 | 0.000b | |
| 222.066 | 4.828 | ||||
| 586.636 | |||||
| Coefficientsa | Beta | Std. Error | Standardized Beta | t | Significance |
| (Constant) | 25.346 | 2.362 | 10.733 | 0.000 | |
| Percent urban | 0.015 | 0.027 | 0.062 | 0.548 | 0.586 |
| Land use freedom | -30.063 | 6.165 | -0.511 | -4.876 | 0.000 |
| Per capita spending on road construction | -0.005 | 0.001 | -0.421 | -4.135 | 0.000 |
The data suggests a recommendation: rather than put more government staff time and millions of dollars into figuring out what property owners can or can’t do with their own property and money, perhaps congestion might be more efficiently addressed by increasing freedom to allow some spontaneous order — that, and building the occasional road couldn’t hurt.